CN117076613A - Electric digital data processing system based on Internet big data - Google Patents
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Abstract
An internet big data based electronic digital data processing system comprising: the system comprises a data acquisition module, a data storage module, a data processing module, a data analysis module and a data visualization module; the invention collects and stores electric digital data through a data collection module, carries out end-to-end learning and deducing by a deep learning unit by utilizing a deep neural network technology, extracts high-level abstract features and expressions, trains and identifies images in the electric digital data through a convolutional neural network, realizes automatic image classification or object identification, carries out tasks such as feature extraction, classification and prediction of the electric digital data, and carries out analysis on the extracted data and visualizes the electric digital data into the forms of charts, graphs, maps and the like through a data analysis module and a data visualization module, so that a user can intuitively understand the data and find potential modes and trends.
Description
Technical Field
The invention relates to the technical field of electric data processing, in particular to an electric digital data processing system based on Internet big data.
Background
Electronic data processing (EDP, electronic data processing) evolved from "data processing" which was generated when most of the calculations were entered into the computer in the form of a punched card and output in the form of a punched card or paper report. Computers are used to replace manual processing of routine data and report forms are generated to support the work activities of the organization. The content is focused on replacing repetitive manual operations to support basic level managers and operators, etc., such as accounting applications, so that it is a re-efficient information system.
An existing electronic data processing technology, for example, chinese patent document CN113794725a describes an electric digital data transmission method and system, comprising: acquiring initial information of current electric digital data, and comparing the electric digital data at the same time with the initial information through an encryption algorithm to obtain differentiated data; performing encryption diagnosis on the differentiated data by adopting a multi-element projection dimension reduction algorithm, and then decoding the diagnosed data; preprocessing the decoded data, marking the preprocessed data by adopting a ciphertext setting technology, and then transmitting the electric digital data. The method can be used for carrying out on-line monitoring, analysis and transmission on the electric digital data by improving the traditional analysis mode. In the scheme, the electric digital parameters of different types are difficult to extract and divide when being collected, processed and analyzed, and the object identification is inconvenient.
Disclosure of Invention
The invention aims to solve the technical problem of providing an electric digital data processing system based on Internet big data, which is used for solving the problems that different kinds of electric digital parameters are difficult to extract and divide characteristics and inconvenient to identify objects when electric digital is collected, processed and analyzed in the existing scheme.
In order to solve the technical problems, the invention adopts the following technical scheme:
an electric digital data processing system based on Internet big data comprises a data acquisition module, a data storage module, a data processing module, a data analysis module and a data visualization module which are connected in sequence;
the data acquisition module is responsible for acquiring electric digital data from external equipment or a sensor, and preprocessing and converting the electric digital data;
the data storage module is used for storing electric digital data and comprises a database, a file system and a cloud storage unit so as to ensure the safety and accessibility of the data;
the data processing module is used for executing data processing tasks, including data cleaning, conversion, aggregation, calculation and convolutional neural network model training, so as to obtain useful information and results, and comprises a natural language processing unit, a deep learning unit and an anomaly detection unit;
the data analysis module is used for analyzing the electric digital data by utilizing statistical analysis, machine learning and data mining technologies, extracting modes, relevance and data insight, and supporting decision making and prediction;
and the data visualization module is used for visualizing the electric digital data into the forms of charts, graphs and maps.
The natural language processing unit is used for processing word segmentation, part-of-speech tagging, syntactic analysis, entity identification, text classification and emotion analysis.
The deep learning unit performs data collection and preprocessing, constructs a convolutional neural network model, data training and optimization, model evaluation and testing, and model application and prediction, and trains and identifies images in the electric digital data through the convolutional neural network.
The construction convolutional neural network comprises a convolutional layer, an activation function and a pooling layer.
The above-mentioned convolution layer is used for extracting the characteristic of the picture or other two-dimensional data, the expression of the convolution layer is:
；
wherein y is 1 (i, j) represents the value of the position (i, j) of the output feature map after convolution, x (i, j) represents the value of the position (i, j) of the input feature map, W represents the weight parameter of the convolution kernel, b represents the bias parameter, and f represents the activation function.
The activation function is used for introducing nonlinear characteristics and enhancing the expression capacity of the neural network, and the formula of the activation function is as follows:
；
wherein y represents the output value after activation, x represents the input value, and activation represents the activation function.
The pooling layer is used for reducing the space size of the feature map and simultaneously retaining the feature information, and the pooling operation has the following formula:
；
wherein y is 2 (i, j) represents the value of the position (i, j) of the pooled output feature map, x (i, j) represents the value of the position (i, j) of the input feature map, pooling represents the pooling operation, and is the maximum value or the average value; feature extraction, classification and prediction of the deep learning model on the electric digital data are realized through convolution, and automatic image classification or object recognition is realized.
The invention provides an electric digital data processing system based on Internet big data, which has the beneficial effects that:
(1) The invention collects and stores electric digital data through the data collection module and the data storage module, and performs end-to-end learning and deducing by using a deep learning unit and a deep neural network technology, processes complex electric digital data, extracts high-level abstract features and expressions, trains and identifies images in the electric digital data through a convolutional neural network, and realizes automatic image classification or object identification, feature extraction, classification, prediction and other tasks of the electric digital data.
(2) According to the invention, the data analysis module and the data visualization module are used for analyzing the extracted data and visualizing the electric digital data into the forms of charts, graphs, maps and the like, so that a user can intuitively understand the data and find potential modes and trends, and an anomaly detection algorithm is used for carrying out real-time monitoring and anomaly detection on the electric digital data and identifying an anomaly mode, an outlier or a fault so as to improve the safety and stability of the system.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a block diagram of an Internet big data based electrical digital data processing system of the present invention;
FIG. 2 is a block diagram of the components of the data processing module of the present invention;
fig. 3 is a block diagram of the convolutional neural network of the present invention.
Detailed Description
The technical scheme of the invention is described in detail below with reference to the accompanying drawings and examples.
As shown in fig. 1, 2 and 3, an electric digital data processing system based on internet big data comprises a data acquisition module, a data storage module, a data processing module, a data analysis module and a data visualization module which are connected in sequence;
the data acquisition module is responsible for acquiring electric digital data from external equipment or a sensor, and preprocessing and converting the electric digital data;
the data storage module is used for storing electric digital data and comprises a database, a file system and a cloud storage unit so as to ensure the safety and accessibility of the data;
the data processing module is used for executing data processing tasks, including data cleaning, conversion, aggregation, calculation and convolutional neural network model training, so as to obtain useful information and results, and comprises a natural language processing unit, a deep learning unit and an anomaly detection unit;
the data analysis module is used for analyzing the electric digital data by utilizing statistical analysis, machine learning and data mining technologies, extracting modes, relevance and data insight, and supporting decision making and prediction;
and the data visualization module is used for visualizing the electric digital data into the forms of charts, graphs and maps, so that a user can intuitively understand the data and find potential modes and trends.
The natural language processing unit is used for processing word segmentation, part-of-speech tagging, syntactic analysis, entity identification, text classification and emotion analysis.
Specifically, word segmentation divides text into words, phrases or other meaningful language units for text preprocessing and text analysis tasks;
the part of speech label is that each word in the text labels part of speech, such as nouns, verbs, adjectives and the like, and can be used for tasks such as grammar analysis, semantic analysis, information extraction and the like;
syntactic analysis is to analyze the grammar structure of sentences, identify phrases, clauses, dependency relationships and the like in the sentences, and analyze the composition and grammar rules which are helpful for understanding the sentences;
the entity identification is used for identifying named entities in the text, such as person names, place names, organization and the like, and can be used for tasks such as information extraction, relation extraction, entity linking and the like;
text classification classifies texts according to predefined categories, and is widely applied to the fields of emotion analysis, spam filtering, topic classification and the like;
emotion analysis is used to identify emotional trends in text, such as positive, negative, or neutral, and is commonly used in the field of social media analysis, etc.
The deep learning unit performs data collection and preprocessing, constructs a convolutional neural network model, data training and optimization, model evaluation and testing, and model application and prediction, and trains and identifies images in the electric digital data through the convolutional neural network.
The process of constructing the convolutional neural network model, training the data and optimizing the data comprises a convolutional layer, an activation function and a pooling layer.
The above convolution layer is used to extract the features of an image or other two-dimensional data, and the formula is as follows:
；
wherein y is 1 (i, j) represents the value of the position (i, j) of the output feature map after convolution, x (i, j) represents the value of the position (i, j) of the input feature map, W represents the weight parameter of the convolution kernel, b represents the bias parameter, and f represents the activation function.
The activation function is used for introducing nonlinear characteristics and enhancing the expression capacity of the neural network, and the common activation function comprises ReLU, sigmoid, tanh, reLU, namely Rectified Linear Unit, and the formula of the activation function is as follows:
；
wherein y represents the output value after activation, x represents the input value, and activation represents the activation function.
The Pooling layer is used for reducing the space size of the feature map and simultaneously retaining the feature information, and common Pooling operations comprise Max Pooling and Average Pooling, and the formulas of the Pooling operations are as follows:
；
wherein y is 2 (i, j) represents the value of the position (i, j) of the pooled output feature map, and x (i, j) represents the inputThe value of the position (i, j) of the feature map, pooling, represents a pooling operation, being a maximum or average; feature extraction, classification and prediction of the deep learning model on the electric digital data can be realized through convolution, and automatic image classification or object recognition is realized.
It can be seen from the above that, through the data acquisition module, the data storage module acquires and stores the electric digital data, and through the deep learning unit, the deep neural network technology is utilized to learn and infer end to end, process complex electric digital data, extract high-level abstract features and expressions, train and identify images in the electric digital data through the convolutional neural network, and realize automatic image classification or object identification, and perform tasks such as feature extraction, classification and prediction on the electric digital data;
the data analysis module and the data visualization module are used for analyzing the extracted data and visualizing the electric digital data into the forms of charts, graphs, maps and the like, so that a user can intuitively understand the data and find potential modes and trends, and the abnormal detection unit is used for monitoring and detecting the electric digital data in real time and identifying abnormal modes, outliers or faults so as to improve the safety and stability of the system.
Claims (7)
1. An electric digital data processing system based on Internet big data is characterized by comprising a data acquisition module, a data storage module, a data processing module, a data analysis module and a data visualization module which are connected in sequence;
the data acquisition module is responsible for acquiring electric digital data from external equipment or a sensor, and preprocessing and converting the electric digital data;
the data storage module is used for storing electric digital data and comprises a database, a file system and a cloud storage unit, and the data storage module is used for ensuring the safety and accessibility of the data;
the data processing module is used for executing data processing tasks, including data cleaning, conversion, aggregation, calculation and convolutional neural network model training, and comprises a natural language processing unit, a deep learning unit and an anomaly detection unit;
the data analysis module is used for analyzing the electric digital data by utilizing statistical analysis, machine learning and data mining technologies, extracting modes, relevance and data insight, and supporting decision making and prediction;
and the data visualization module is used for visualizing the electric digital data into the forms of charts, graphs and maps.
2. The system of claim 1, wherein the natural language processing unit is configured to process word segmentation, part-of-speech tagging, syntactic analysis, entity recognition, text classification, and emotion analysis.
3. The internet big data-based electric digital data processing system of claim 2, wherein the deep learning unit performs data collection and preprocessing, construction of a convolutional neural network model, data training and optimization, model evaluation and testing, and model application and prediction, and trains and identifies images in the electric digital data through the convolutional neural network.
4. An internet big data based digital data processing system according to claim 3, wherein said constructing a convolutional neural network comprises a convolutional layer, an activation function, and a pooling layer.
5. The system of claim 4, wherein the convolution layer is used to extract the features of the image or other two-dimensional data, and the expression of the convolution layer is:
；
wherein y is 1 (i, j) represents the value of the position (i, j) of the convolved output feature map, and x (i, j) represents the position (i) of the input feature mapThe value of j) is calculated,weight parameters representing convolution kernels, +.>Representing the bias parameters +.>Representing an activation function.
6. The system of claim 5, wherein the activation function is used to introduce nonlinear characteristics to enhance the expressive power of the neural network, and the formula of the activation function is as follows:
；
wherein,representing the output value after activation,/->Representing the input value +.>Representing an activation function.
7. An internet big data based digital data processing system according to claim 6, wherein the pooling layer is configured to reduce the spatial size of the feature map while preserving the feature information, and the pooling operation is formulated as follows:
；
wherein y is 2 (i, j) represents poolingThe value of the position (i, j) of the output feature map after that, x (i, j) represents the value of the position (i, j) of the input feature map,representing the pooling operation as a maximum or average; feature extraction, classification and prediction of the deep learning model on the electric digital data can be realized through convolution, and automatic image classification or object recognition is realized.
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